Dispersion Calculator

This program calculates an Offense Dispersion Index and associated values for specific crime data in a fixed format. This software never really left BETA mode, which officially means that it is still being tested (though it will most likely never progress to full-blown superness). I offer no warranty whatsoever to either the functionality of this software or to the suitability for your purpose. While there is no deliberate malicious code in there from me (I can’t speak for Microsoft), please be aware that I offer no warranty and you might have that one-in-a-million hardware configuration that causes this program to erase your hard drive, fry your monitor and electrocute your dog.

Input data

Input data are very easy to create. There is only one input format, and only one file type. Your data need to be in a csv file (comma separated values) and with three columns:

The name or reference code of the area

The crime volume (or rate) at the first time period

The crime volume (or rate) at the second time period

You should start with a single header row, and then the data. For example:

Once the data are ready, start the program. The program is downloaded as a zip file. Once you have unzipped the single file, double-click the executable file (dispersion.exe) and then click the green button. The other buttons give a brief help on the input format and close the program respectively.

The analysis should be very quick. When it has finished, if you have Microsoft Excel, the program will offer to try and open the resulting output file for you. You have no control over the filename. The output file takes the name of the input file and simply adds the year, month, date and time of the analysis, with a csv extension. The use of the time prevents multiple analyses overwriting each other. If Excel fails to open the output file, you can find the file in the same folder as your data source file. If desperate, it will open with Notepad.

Results file

The output will look something like this:

PICTURE HERE

From the top, the file contains:

Input file: The name of the source data that you selected.

Absolute difference from t1 to t2: The change in total crime for all areas from the first time period to the second.

Crime rate change: The absolute difference expressed as a % of the first time period.

Rate became negative after removal of: The number of areas that would have to be removed before a positive crime change would become a crime decrease.

The Offence Dispersion Index: Most easily explained as the ratio of increase-causing areas, to total areas in the analysis. See ‘the paper’ below.

Areas performing worse: Irrespective of whether they contributed directly to the crime increase for the whole area, this is the number of areas that had a crime increase.

Total areas: If you can open and run a C# program with geospatial data for different time periods, I think you can figure this out.

Non-contributory dispersion index (NCDI): Read the paper below.

The table follows. Each table column is explained here:

NRemoved: The ranking of the area being removed.

Where: The name or code for the area. In the example above, the first area is sector 9D.

PctChange: The percentage change for the whole study area after removal of the current area. Although the example city above started with a 2.885% crime increase, removal of sector 9D reduced this to a crime increase of 2.562%.

Crime_t1: Total crime for time 1 after exclusion of this area. For example, after removal of sector 9D the crime in t1 went down to 38,831.

Crime_t2: Total crime for time 2 after exclusion of this area. For example, after removal of sector 9D the crime in t2 went down to 39,826.

AbsDif: The remaining absolute difference in crime volume from t1 to t2 on removal of the area.

Area_contrib: How much actual crime the area contributed to the city-wide increase.

Area_t1: The count of crime in area (in this case a sector) at t1.

Area_t2: The count of crime in area (in this case a sector) at t2.

PctAreaChg: The percentage change recorded in the single area (in the example, a sector) from t1 to t2.

If you wish to map values in GeoDa or similar, then mapping the NRemoved value with LISA statistics can identify clusters of areas.

The paper: The spatial dependency of crime increase dispersion

If you wish to learn more, please read the following research article. It would be appreciated if you cite it in any research undertaken with the software.

Technical support

Please be advised – in the nicest possible way – that due to workload, university administration, students, research projects, social life, travel, flying my seaplane (Little Nellie), consultancy work, speaking engagements, vacations, sickness, home renovations, a wife and two manic kittens, and the myriad other things that are going on in my chaotic and shambolic life, I am not able to offer any technical support whatsoever. It works here, so if it doesn’t work at your end then it is probably an issue of permissions, network security or a whole range of other technical problems that I can’t fix. Please see your system administrator. If however you want to tell me that it works great and you have found it to be really useful, and you have no expectation of a response, then please – email away!

Finally, if there is a logical fault with the program and it works but the calculations are wrong, then none of the above applies. I’d like to hear about it.